Too Much of a Good Thing: When sim2real Efforts Impede Policy Learning (And What to Do About It)
arXiv:2606.02636v1 Announce Type: cross Abstract: While sim2real efforts are necessary for effective policy transfer to hardware, there is such a thing as too much of a good thing. We argue that sim2real efforts have led to misaligned incentives with policy learning, resulting in simulator lock in and poor policy exploration due to the unreasonable constraints imposed by the real world. We offer a diagnosis and explanation of the current status of the problem, and propose a potential solution via a sim2sim2real paradigm that leverages the robot's kinematics as the sole design constraint.
This paper highlights a critical challenge emerging in advanced robotics and AI development, as the drive for realistic simulation for real-world deployment faces diminishing returns and misaligned incentives.
A strategic reader should care because this research directly impacts the speed and efficiency of bringing AI-powered robotic systems from development to commercialization and widespread adoption.
The proposed 'sim2sim2real' paradigm suggests a shift in how robotics development approaches data generation and policy learning, potentially accelerating robot capabilities by de-emphasizing overly realistic, yet restrictive, initial simulations.
- · AI agents developers
- · Robotics research labs
- · Companies seeking faster robot deployment
- · Traditional sim2real methodologies
- · Simulation platform developers focused solely on realism
Robotics development pipelines may adopt tiered simulation approaches, moving away from monolithic, high-fidelity simulators for early-stage policy learning.
This could lead to a significant acceleration in the capabilities and robustness of deployed robotic systems, particularly those operating in dynamic, unstructured environments.
More rapid and effective policy learning could reduce the cost and time-to-market for complex robotic solutions, potentially fueling broader adoption across industries.
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Read at arXiv cs.AI